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refactoring
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Colombo
parent
d6a685887f
commit
3b6ad4abf9
4 changed files with 65 additions and 52 deletions
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@ -46,9 +46,11 @@ class IEPolys:
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def add(self, type):
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self.list = self.list[0:self.n]
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self.list.append ( IEPolysPoints(self, type) )
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l = IEPolysPoints(self, type)
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self.list.append ( l )
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self.n_max = self.n = self.n + 1
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self.dirty = True
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return l
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def n_dec(self):
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self.n = max(0, self.n-1)
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@ -370,7 +370,7 @@ class QModel(ModelBase):
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sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.ALL_HULL, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}
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],
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generators_count=src_generators_count ),
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@ -378,7 +378,7 @@ class QModel(ModelBase):
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sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.ALL_HULL, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution}
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],
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generators_count=dst_generators_count )
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])
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@ -536,7 +536,7 @@ class SAEHDModel(ModelBase):
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['random_warp'], 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'ct_mode': ct_mode, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.ALL_EYES_HULL, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=src_generators_count ),
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@ -544,7 +544,7 @@ class SAEHDModel(ModelBase):
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':self.options['random_warp'], 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.ALL_EYES_HULL, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False , 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE_EYES, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=dst_generators_count )
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])
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@ -28,10 +28,9 @@ class SampleProcessor(object):
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class FaceMaskType(IntEnum):
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NONE = 0
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ALL_HULL = 1 #mask all hull as grayscale
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EYES_HULL = 2 #mask eyes hull as grayscale
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ALL_EYES_HULL = 3 #combo all + eyes as grayscale
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STRUCT = 4 #mask structure as grayscale
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FULL_FACE = 1 #mask all hull as grayscale
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EYES = 2 #mask eyes hull as grayscale
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FULL_FACE_EYES = 3 #combo all + eyes as grayscale
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class Options(object):
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def __init__(self, random_flip = True, rotation_range=[-10,10], scale_range=[-0.05, 0.05], tx_range=[-0.05, 0.05], ty_range=[-0.05, 0.05] ):
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@ -53,13 +52,25 @@ class SampleProcessor(object):
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for sample in samples:
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sample_face_type = sample.face_type
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sample_bgr = sample.load_bgr()
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sample_landmarks = sample.landmarks
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ct_sample_bgr = None
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h,w,c = sample_bgr.shape
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def get_full_face_mask():
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if sample.eyebrows_expand_mod is not None:
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full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks)
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return np.clip(full_face_mask, 0, 1)
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def get_eyes_mask():
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eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample_landmarks)
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return np.clip(eyes_mask, 0, 1)
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is_face_sample = sample.landmarks is not None
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is_face_sample = sample_landmarks is not None
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if debug and is_face_sample:
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LandmarksProcessor.draw_landmarks (sample_bgr, sample.landmarks, (0, 1, 0))
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LandmarksProcessor.draw_landmarks (sample_bgr, sample_landmarks, (0, 1, 0))
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if sample_face_type == FaceType.MARK_ONLY:
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warp_resolution = np.max( [ opts.get('resolution', 0) for opts in output_sample_types ] )
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@ -81,6 +92,15 @@ class SampleProcessor(object):
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ct_mode = opts.get('ct_mode', None)
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data_format = opts.get('data_format', 'NHWC')
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if sample_type == SPST.FACE_MASK:
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border_replicate = False
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elif sample_type == SPST.FACE_IMAGE:
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border_replicate = True
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border_replicate = opts.get('border_replicate', border_replicate)
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borderMode = cv2.BORDER_REPLICATE if border_replicate else cv2.BORDER_CONSTANT
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if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
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if not is_face_sample:
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raise ValueError("face_samples should be provided for sample_type FACE_*")
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@ -97,49 +117,30 @@ class SampleProcessor(object):
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if sample_type == SPST.FACE_IMAGE or sample_type == SPST.FACE_MASK:
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if sample_type == SPST.FACE_MASK:
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if face_mask_type == SPFMT.ALL_HULL or \
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face_mask_type == SPFMT.EYES_HULL or \
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face_mask_type == SPFMT.ALL_EYES_HULL:
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if face_mask_type == SPFMT.ALL_HULL or \
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face_mask_type == SPFMT.ALL_EYES_HULL:
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if sample.eyebrows_expand_mod is not None:
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all_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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all_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample.landmarks)
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all_mask = np.clip(all_mask, 0, 1)
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if face_mask_type == SPFMT.EYES_HULL or \
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face_mask_type == SPFMT.ALL_EYES_HULL:
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eyes_mask = LandmarksProcessor.get_image_eye_mask (sample_bgr.shape, sample.landmarks)
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eyes_mask = np.clip(eyes_mask, 0, 1)
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if face_mask_type == SPFMT.ALL_HULL:
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img = all_mask
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elif face_mask_type == SPFMT.EYES_HULL:
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img = eyes_mask
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elif face_mask_type == SPFMT.ALL_EYES_HULL:
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img = all_mask + eyes_mask
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elif face_mask_type == SPFMT.STRUCT:
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if sample.eyebrows_expand_mod is not None:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample.landmarks)
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if sample_type == SPST.FACE_MASK:
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if face_mask_type == SPFMT.FULL_FACE:
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img = get_full_face_mask()
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elif face_mask_type == SPFMT.EYES:
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img = get_eyes_mask()
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elif face_mask_type == SPFMT.FULL_FACE_EYES:
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img = get_full_face_mask() + get_eyes_mask()
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else:
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img = np.zeros ( sample_bgr.shape[0:2]+(1,), dtype=np.float32)
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if sample.ie_polys is not None:
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sample.ie_polys.overlay_mask(img)
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if sample_face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, warp_resolution, face_type)
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
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img = cv2.warpAffine( img, mat, (warp_resolution, warp_resolution), flags=cv2.INTER_LINEAR )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_LINEAR )[...,None]
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else:
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=False, cv2_inter=cv2.INTER_LINEAR)
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR )[...,None]
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate, cv2_inter=cv2.INTER_LINEAR)
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_LINEAR )[...,None]
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if channel_type == SPCT.G:
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out_sample = img.astype(np.float32)
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@ -172,14 +173,14 @@ class SampleProcessor(object):
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img = cv2.GaussianBlur(img, (gblur_rnd_kernel,) *2 , 0)
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if sample_face_type == FaceType.MARK_ONLY:
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, warp_resolution, face_type)
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)
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img = cv2.warpAffine( img, mat, (warp_resolution,warp_resolution), flags=cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
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img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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else:
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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mat = LandmarksProcessor.get_transform_mat (sample.landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=cv2.BORDER_REPLICATE, flags=cv2.INTER_CUBIC )
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img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=border_replicate)
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mat = LandmarksProcessor.get_transform_mat (sample_landmarks, resolution, face_type)
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img = cv2.warpAffine( img, mat, (resolution,resolution), borderMode=borderMode, flags=cv2.INTER_CUBIC )
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img = np.clip(img.astype(np.float32), 0, 1)
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@ -230,7 +231,7 @@ class SampleProcessor(object):
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# img = imagelib.warp_by_params (params, img, warp, transform, can_flip=True, border_replicate=True)
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# img = cv2.resize( img, (resolution,resolution), cv2.INTER_CUBIC )
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elif sample_type == SPST.LANDMARKS_ARRAY:
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l = sample.landmarks
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l = sample_landmarks
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l = np.concatenate ( [ np.expand_dims(l[:,0] / w,-1), np.expand_dims(l[:,1] / h,-1) ], -1 )
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l = np.clip(l, 0.0, 1.0)
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out_sample = l
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@ -255,6 +256,16 @@ class SampleProcessor(object):
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return outputs
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"""
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STRUCT = 4 #mask structure as grayscale
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elif face_mask_type == SPFMT.STRUCT:
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if sample.eyebrows_expand_mod is not None:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
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else:
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img = LandmarksProcessor.get_face_struct_mask (sample_bgr.shape, sample_landmarks)
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close_sample = sample.close_target_list[ np.random.randint(0, len(sample.close_target_list)) ] if sample.close_target_list is not None else None
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close_sample_bgr = close_sample.load_bgr() if close_sample is not None else None
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